1. Field of the Invention
The present invention relates in general to a method and apparatus for searching, browsing and summarizing moving image data, and more particularly to a method and apparatus for searching, browsing and summarizing moving image data using the fidelity of a tree-structured moving image hierarchy, which can partition or divide the moving image data into predetermined units (for example, shots or segments), extract a key frame from each of the partitioned or divided units, create a tree-structured key frame hierarchy on the basis of the extracted key frames, store a fidelity value of each of the key frames, which is a sub-tree information representative degree of each key frame, at an edge between adjacent ones of the key frames, and effectively and efficiently search, browse and summarize the moving image data using the stored fidelity values.
2. Description of the Prior Art
Conventional methods for searching and browsing moving image data have carried out the searching and browsing using key frames. However, such conventional searching and browsing methods have had a limitation in effectively and efficiently searching and browsing moving image data, because they did not consider the fidelity of key frames. One such method may be, for example, a still image searching and browsing method proposed by Purdue University, which is capable of raising a searching speed by a considerable degree using a branch and bound searching technique based on triangle inequality obtained by applying tree-structured vector quantization (TSVQ) to still image data. The browsing method proposed by Purdue University is an active browsing method based on a hierarchical similarity pyramid, each level of which includes a cluster of similar images created in a 2-D grid. The similarity pyramid has a smaller cluster at its lower level and a structure representative of each image at its lowest level. This proposed browsing method has pruning and reorganization functions as its fundamental functions, thereby effectively performing the browsing operation on the basis of user relevance feedback information. However, the above-mentioned browsing method is only a one-sided mode of a server and provides no benchmark for a user to determine how effective the browsing is.
In conclusion, the still image searching and browsing method proposed by Purdue University merely leads a user to a one-sided still image searched result of a server without setting a threshold value for a user's satisfactory level. Further, the proposed browsing method extracts key frames from moving image data and performs the browsing operation using the extracted key frames, but does not present how effectively the browsing operation represents the moving image data. Moreover, this searching and browsing method is not very efficient because it utilizes not one tree structure, but individual tree structures formed by different mechanisms.
On the other hand, considering studies on rate-constraints moving image summary, there has been proposed a key frame extraction technology based on a restriction in time. However, this technology provides not a moving image summary method for satisfying a user's desired time, but an algorithm for extracting respective key frames from clusters of similar frames at intervals of a predetermined threshold range, or a predetermined period of time or more. In other words, the proposed key frame extraction technology does not provide a moving image summary method capable of summarizing, for example, a two hours-required moving image to a ten minutes-required amount. In conclusion, there is a pressing need for the development of a moving image search, browsing and summary method capable of effectively summarizing moving image data to the amount required for a time period desired by a user.